• Structural Engineering and Mechanics
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• v.58 no.1
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• pp.21-38
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• 2016

Many studies have been carried out to investigate the important factors in calculating the realistic entropy amount of water distribution networks, but none of them have considered both mechanical and hydraulic characteristics of the networks. Also, the entropy difference in various networks has not been calculated exactly. Therefore, this study suggested a modified entropy function to calculate the informational entropy of water distribution networks so that the order of demand nodes and entropy difference among various networks could be calculated by taking into account both mechanical and hydraulic characteristics of the network. This modification was performed through defining a coefficient in the entropy function as the amount of outflow at each node to all dissipated power in the network. Hence, a more realistic method for calculating entropy was presented by considering both mechanical and hydraulic characteristics of network while keeping simplicity. The efficiency of the suggested method was evaluated by calculating the entropy of some sample water networks using the modified function.

• Journal of Wetlands Research
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• v.11 no.2
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• pp.89-98
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• 2009

This study determines the stream mean slope, stream slope and longitudinal stream profile based on the concept of informational entropy. Maximizing the entropy will make the probability distribution of longitudinal stream profile as uniform as possible while satisfying the constraints. Using this relationships the mean stream slope, stream slope and longitudinal stream profile formulas were derived. The parameters of the applied streams were estimated by the least square method using the geomorphological factors of Dalchon stream basin obtained from Chungcheong Buk-Do local stream consolidation scheme drawings. The comparative investigation was performed between the observed and simulated mean stream slope and longitudinal stream profile, and are in good agreement with the measured data. It is noted that this results can be used in the estimation of stream mean slope and longitudinal stream profile.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.509-516
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• 2020

Since the 1960s, many rivers have been polluted and destroyed due to river repair projects for economic development and the covering of small rivers due to urbanization. Many studies have analyzed rivers using measured river topographic factors, but surveying is not easy when the flow rate changes rapidly, such as during a flood. In addition, the previous research has been mainly about the cross section of a river, so information on the longitudinal profile is insufficient. This research used informational entropy theory to obtain an equation that can calculate the average river slope, river slope, and river longitudinal elevation for a river basin in real time. The applicability was analyzed through comparison with measured data of a river's characteristic factors obtained from a river plan. The parameters were calculated using informational entropy theory, nonlinear regression analysis, and actual data. The longitudinal elevation entropy equation for each stream was then calculated, and so was the average river slope. All of the values were over 0.96, so it seems that reliable results can be obtained when calculating river characteristic factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.325-333
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• 2020

Current methods of measuring the sediment concentration of natural streams can be affected by weather conditions and have lower reliability in bed-load sections due to mechanical limits. Theoretical methods have to be used to solve this problem, but they have low reliability compared to the measured values and diverse results for the bed-load sediment concentration. This study proposes a new way to reliably determine the bed-load sediment concentration from the relation with theoretical depth-integrated concentration based on the informational entropy concept. Sediment distribution shows a uniform probability distribution under maximized entropy conditions under some constraints, so a function can be calculated for the sediment distribution and depth-integrated concentration. The parameters of a stream were estimated by a nonlinear regression method using the concentration data from a past experiment. Equilibrium N (EN) was estimated using the relation between two different formulas proposed in this study, which can ease the estimation of both the total sediment distribution and depth-integrated sediment concentration with high reliable results with an average R2 of 0.924.